Estimation of natural gases water content using adaptive neuro-fuzzy inference system

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Publication details

Baghban, A, Kashiwao, T, Bahadori, M, Ahmad, Z & Bahadori, A 2016, Petroleum Science and Technology, vol. 34, no. 10, pp. 891-897.

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To appropriate design and satisfactory performance of utilities in the gas processing and transmission plants, a crucial factor that should be taken in consideration is the natural gas water content. The present research aimed to develop a precise strategy for estimating sour gas/sweet gas water content ratio. This developed predictive tool is based on adaptive neuro-fuzzy inference system (ANFIS). In this regard, a comprehensive data bank that contains 1,126 data points was collected. This model predicts ratio of sour gas to sweet gas as function of pressure, temperature, and equilibrium H2S mole fraction. The ranges of pressure and temperature were 200–70000 KPa and 10–150°C, respectively. In addition, the equilibrium H2S mole fraction ranges between 0.076 and 0.492. Results obtained from the ANFIS model confirmed acceptable and reasonable predictive capability of this model. This tool is simple to use and can be help petroleum engineers to predict water content of natural gas at broad ranges of conditions.